INVESTIGATING EROSION RISK, VULNERABILITY, AND POTENTIAL: AN AHP APPROACH WITH GIS

John H. CartwrightMississippi State University

INTRODUCTION

The purpose of this research is to utilize geospatial technologies for improved decision making with regards to soil erosion risk, vulnerability, and potential in Gulf of Mexico coastal watersheds (estuarine drainage areas) as it relates to landscape changes.

Geographic Information Systems (GIS) and associated technologies can help in understanding the cumulative effects of drivers as they relate to soil erosion risk, vulnerability, and potentialin coastal watersheds.

The Analytical Hierarchy Process (AHP) in combination with a GIS has proven to be an important tool for environmental assessments. Considerable attention has been given to the combination by multidisciplinary decision makers.

BACKGROUND

Sediment is the number one volumetric constituent in terms of non-point source pollutants (Basnyat, et al. 1999) with an estimated 95% trapped in coastal estuaries and wetlands (Meade, 1982).

Erosion (and resulting sedimentation) across the landscape and within the hydrologic systems can be directly related to the type of ground cover, terrain morphology, soil types , and amount of precipitation.

Increases surface water turbidity.

Interferes with navigation and increases flooding risk.

Increases nutrient loading, promoting harmful algal blooms and aquatic weed growth.

Increased surface runoff.

Introduces additional hazardous substances, accumulates in sediment posing risk to consumers of benthic organisms.

Contamination of surface water supplies.

Loss of habitat and ecosystem functions and services.

Detrimental economic impacts.

STUDY AREA

Drainage basins includeda) Eastern Mississippi Soundb) Mobile Bayc) Apalachicola Bay

These sites are host to National Estuarine Research Reserves and offers an area of study that provides basin level landscapes with diverse characteristics.

ANALYTICAL HIERARCHY PROCESS

AHP is a mathematical modeling technique for multi-criteria decision making and analysis developed by T.L. Saaty in the early 1970’s.

The AHP method helps to specify numerical weights representing the importance of criteria or input variables in the process.

AHP allows for both qualitative and quantitative approaches to solve problems based on expert knowledge of criteria interactions.

Intensity of Importance

Definition

1 Equal Importance of both components

3 Judgment slightly favors one component over another (moderate difference of importance)

5 Judgement strongly favors one component over another (strong difference of importance)

7 Very Strong or demonstrated importance of one component with respect to another

9 Evidence of extreme difference of importance of one component with respect to another

MODEL LAYERS

Data Layer GIS Data Type Raster Data Product Data Source

Elevation Grid (30 Meter) Slope -30m USGS National Elevation Dataset

Soils Polygon Erosion (K-Factor)-30m USDA Soil Survey Geographic Database

Hydrology Line Stream Density-30m USGS National Hydrography Dataset

Land Cover Grid (30 Meter) Land-Use/Practice-30m MRLC National Land Cover Database

Precipitation Grid (4 Kilometer) Annual Precipitation-30m PRISM Climate Group

CONCEPTUAL MODEL

Physical Erodibility Slope Stream Density Soil K-factor

Land Sensitivity Land Use Land Practices

Precipitation Erositivity 30 Year Rainfall Averages

K-factor

Density

Slope

Practice

Land Use

P-Erode

L-Sensitivity

P-Erositivity

T-Erode

PAIRWISE COMPARISON

CR Value = 0.086 OKPairwise comparisonsCriteria Slope Drainage Density K-Factor Land Use Land PracticeSlope 1.00 7.00000 3.00000 2.00000 5.00000Drainage Density 0.14 1.00 0.33333 0.33333 0.20000K-Factor 0.33 3.00 1.00 0.25000 0.33333Land Use 0.50 3.00 4.00 1.00 2.00000Land Practice 0.20 5.00 3.00 0.50 1.00Sum 2.18 19.00 11.33 4.08 8.53

STANDARDIZED MATRIXSlope Drainage Density K-Factor Land Use Land Practice Weight

Slope 0.46 0.37 0.26 0.49 0.59 43.4%Drainage Density 0.07 0.05 0.03 0.08 0.02 5.1%K-Factor 0.15 0.16 0.09 0.06 0.04 10.0%Land Use 0.23 0.16 0.35 0.24 0.23 24.4%Land Practice 0.09 0.26 0.26 0.12 0.12 17.2%

LAYER ANALYSIS

Slope K-FactorStream Density

LAYER ANALYSIS

Land Use 30 Year PrecipLand Practice

EROSION POTENTIAL

Physical Erodibility Total Erodibility

UPCOMING EFFORTS

Run analysis with continuous data layers instead of categorical/classified data and compare (both) with existing models.

Use precipitation as a driver and run event analysis with MPE (MultisensorPrecipitation Estimates) data.

Continue building base of expert input for criteria used in AHP method.

Include Social Vulnerability Index (SoVI) to begin efforts and exploratory analysis of risk and vulnerability.

John H. Cartwright – johnc@ngi.msstate.edu